Clinical classification and prediction are key components of clinical care. Even modest improvements in classification and predictive performance may have significant healthcare consequences in terms of improved patient outcomes and reduced healthcare costs. This proposal describes a new, efficient Bayesian machine-learning method for performing clinical predictions. The method is designed to use both clinical and genome-wide data in predicting patient outcomes. The method's performance will be evaluated using existing clinical and genome-wide data from the Framingham Heart Study that is being made available to qualified researchers through the SHARe resource of the National Center for Biotechnology Information. The outcomes to be predicted in individuals include the onset of major cardiovascular events and death from all causes. The study will evaluate how well these predictions can be made with a new Bayesian method when using traditional clinical data alone, genome-wide data alone, and both types of data together. The method's performance will also be compared that of existing models and methods. The main hypothesis to be tested is that the proposed machine-learning method will be an advancement over existing methods in that it will be computationally feasible to apply it using a combination of traditional clinical data and genome-wide data, and it will yield better predictive performance than do existing predictive models and methods. If shown to be so, this new method is anticipated to provide substantial benefit in future electronic patient-care systems, including applications to computer-based decision support that have available both traditional clinical data and genome-wide data.